Climate Change and Rural Livelihoods in the Lawra District of Ghana. A Qualitative Based Study
Bibliographic record
Abstract
Climate change is a growing threat to the world's poorest and most vulnerable living in rural areas. The impacts of climate change challenge efforts to reducing poverty and hence, will require new approaches to focus development programming on the changing realities of the world. Understanding how the impacts of climate change affect the people, and their knowledge and experience in coping with it will assist in identifying appropriate strategies for adaptation to it. This paper thus examined the impacts of climate change on livelihoods of rural communities in the Upper West region of Ghana and the challenges posed to efforts at reducing poverty in the area. Discussions on vulnerability to climate variability and adaptation issues in this paper focused on evidence observed by 10 communities in the Lawra District. Adopting a qualitative approach, ten focused group discussions were organized to gather data. Specific issues discussed surrounded evidence of climate change in the communities, its impacts, underlying causes of vulnerability to climate and coping strategies employed by community members. Based on the discussions, the paper recommends the need to develop and intensify effective institutional mechanisms to facilitate community adaptation measures, awareness raising (creation) on anti-environments practices in communities, institution of bye and customary laws to regulate human anti-environmental activities, and the implementation of adaptation projects to aid communities cope with the major impacts of climate change in the district and world at large.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".